Pathway-Aware Template-Based Retrosynthesis

There has been growing interest in developing machine-learning retrosynthesis models to accelerate chemical synthesis, enabling the discovery of routes to synthesize high-value products such as small-molecule drugs. However, most single-step models within a multistep planning algorithm perform recursive predictions without considering the previous reaction steps in the retrosynthetic pathway, which may lead to inefficiencies in the search, as these previous steps can provide important context for deciding which reactions to follow. We introduce the PATRO (Pathway-Aware Template-based RetrOsynthesis) model, which augments a template-based single-step retrosynthesis model by processing pathway-level information with a Long Short-Term Memory (LSTM). Compared to the baseline model, we demonstrate improvements in both single-step and multistep retrosynthesis, which can be attributed to the incorporation of pathway-level information and related architectural modifications. In single-step retrosynthesis, PATRO outperformed the baseline by 2.3% in top-1 accuracy, demonstrating improved template predictions when considering pathway context. After integrating the pathway-aware model as the expansion policy for two multistep retrosynthesis algorithms, we demonstrate that PATRO provides consistent performance gains over the baseline model across multiple multistep metrics, including success rate, patent route recovery rate, and top-k accuracy. The PATRO model also enables more efficient planning to discover the literature routes extracted from patents, requiring on average 10% fewer iterations. While demonstrated here within a well-defined template-based framework, we believe that this strategy of incorporating pathway-level information could benefit other diverse retrosynthesis models.